This repository stores the model artifacts used by speakrs, a fast Rust speaker diarization library.
With the default online feature, speakrs downloads the required files from
this repository on first use. The SDK currently pins revision
5d24ffee75f13fb061fa6d10944a64e2dc1d5e6f.
.mlmodelc bundles for Apple-platform CoreML runs# macOS with CoreML
speakrs = { version = "0.5", features = ["coreml"] }
# NVIDIA GPU
speakrs = { version = "0.5", features = ["cuda"] }
# CPU only
speakrs = "0.5"
use speakrs::{ExecutionMode, OwnedDiarizationPipeline};
fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let mut pipeline = OwnedDiarizationPipeline::from_pretrained(ExecutionMode::CoreMl)?;
let audio: Vec<f32> = load_your_mono_16khz_audio_here();
let result = pipeline.run(&audio)?;
print!("{}", result.rttm("my-audio"));
Ok(())
}
For offline or airgapped setups, download this repository and set
SPEAKRS_MODELS_DIR to the local model directory.
The artifacts are exported or converted for speakrs from the
pyannote community-1
pipeline and its segmentation and WeSpeaker components. PLDA and VBx parameters
are extracted from the pipeline cache. CoreML bundles are converted from the
exported model artifacts for Apple-platform execution.
Upstream model access may require accepting upstream terms. Users are responsible for complying with the licenses and terms of the upstream models and datasets.
9 commits
This repository stores the model artifacts used by speakrs, a fast Rust speaker diarization library.
With the default online feature, speakrs downloads the required files from
this repository on first use. The SDK currently pins revision
5d24ffee75f13fb061fa6d10944a64e2dc1d5e6f.
.mlmodelc bundles for Apple-platform CoreML runs# macOS with CoreML
speakrs = { version = "0.5", features = ["coreml"] }
# NVIDIA GPU
speakrs = { version = "0.5", features = ["cuda"] }
# CPU only
speakrs = "0.5"
use speakrs::{ExecutionMode, OwnedDiarizationPipeline};
fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let mut pipeline = OwnedDiarizationPipeline::from_pretrained(ExecutionMode::CoreMl)?;
let audio: Vec<f32> = load_your_mono_16khz_audio_here();
let result = pipeline.run(&audio)?;
print!("{}", result.rttm("my-audio"));
Ok(())
}
For offline or airgapped setups, download this repository and set
SPEAKRS_MODELS_DIR to the local model directory.
The artifacts are exported or converted for speakrs from the
pyannote community-1
pipeline and its segmentation and WeSpeaker components. PLDA and VBx parameters
are extracted from the pipeline cache. CoreML bundles are converted from the
exported model artifacts for Apple-platform execution.
Upstream model access may require accepting upstream terms. Users are responsible for complying with the licenses and terms of the upstream models and datasets.
9 commits